# codetransformer-python-s

## Model Overview

The `codetransformer-python-s` is a small-scale, decoder-only Transformer model fine-tuned specifically for generating and completing Python code. It is designed for speed and efficiency in environments where resource constraints are a concern, while maintaining a high degree of syntactic correctness and logical coherence for common programming tasks.

## Model Architecture

* **Base Model:** Adapted from a scaled-down GPT-2 variant (similar to 350M parameter size).
* **Architecture:** Causal Transformer (Decoder-only stack). 
* **Task:** Causal Language Modeling. It predicts the next token (line of code, function call, variable name, etc.) given the preceding context.
* **Training Data:** Curated dataset of publicly available, high-quality Python repositories and popular algorithm implementations.

## Intended Use

* **Code Completion:** Providing intelligent, multi-line suggestions within IDEs and code editors.
* **Function Generation:** Generating boilerplate or utility functions from descriptive docstrings or comments.
* **Educational Tool:** Assisting new programmers by demonstrating common language patterns and idiomatic Python usage.

## Limitations and Ethical Considerations

* **Logic Errors:** The model is a text predictor, not a debugger or compiler. Generated code may contain subtle logical or runtime errors.
* **Security Risks:** The model may reproduce insecure or vulnerable code patterns learned from its training data. **Generated code must be thoroughly audited before deployment.**
* **Training Data Dependency:** It is heavily biased towards patterns present in its training corpus and may struggle with highly novel algorithms or external library APIs it has not encountered.
* **Size Limitation:** Being a small model ('-s'), it has a limited context window (`n_ctx=1024`) and may fail to maintain consistency across very large files or complex projects.

## Example Code

To generate Python code given a function signature:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
model_name = "YourOrg/codetransformer-python-s"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the prompt
prompt = "def calculate_factorial(n):\n    \"\"\"Calculates the factorial of a positive integer n.\"\"\"\n    if n == 0:"
input_ids = tokenizer.encode(prompt, return_tensors='pt')

# Generate code
output = model.generate(
    input_ids,
    max_length=100,
    num_return_sequences=1,
    do_sample=True,
    temperature=0.4, # Lower temperature for less creative, more deterministic code
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id
)

generated_code = tokenizer.decode(output[0], skip_special_tokens=False)
print("--- Generated Code Snippet ---")
# Only the generated completion is valuable, but the full sequence is returned
print(generated_code)